Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model

While large-scale adoption of electric vehicles (EVs) globally would reduce carbon dioxide (CO2) and traditional air pollutant emissions from the transportation sector, emissions from the electric sector, refineries, and potentially other sources would change in response. Here, a multi-sector human-Earth systems model is used to evaluate the net long-term emission implications of large-scale EV adoption in the US over widely differing pathways of the evolution of the electric sector. Our results indicate that high EV adoption would decrease net CO2 emissions through 2050, even for a scenario where all electric sector capacity additions through 2050 are fossil fuel technologies. Greater net CO2 reductions would be realized for scenarios that emphasize renewables or decarbonization of electricity production. Net air pollutant emission changes in 2050 are relatively small compared to expected overall decreases from recent levels to 2050. States participating in the Regional Greenhouse Gas Initiative experience greater CO2 and air pollutant reductions on a percentage basis. These results suggest that coordinated, multi-sector planning can greatly enhance the climate and environmental benefits of EVs. Additional factors are identified that influence the net emission impacts of EVs, including the retirement of coal capacity, refinery operations under reduced gasoline demands, and price-induced fuel switching in residential heating and in the industrial sector.

Incorporating upstream emissions into electric sector nitrogen oxide reduction targets

Electricity production is a major source of air pollutants in the U.S. Policies to reduce these emissions typically result in the power industry choosing to apply controls or switch to fuels with lower combustion emissions. However, the life-cycle emissions associated with various fuels can differ considerably, potentially impacting the effectiveness of fuel switching. Life-cycle emissions include emissions from extracting, processing, transporting, and distributing fuels, as well as manufacturing and constructing new generating capacity. The field of life-cycle analysis allows quantification of these emissions. While life-cycle emissions are often considered in greenhouse gas mitigation targets, they generally have not been included in air quality policymaking. We demonstrate such an approach, examining a hypothetical electric sector emission reduction target for nitrogen oxides (NO_x) using the Global Change Assessment Model with U.S. state-level resolution. When only power plant emissions are considered in setting a NOx emission reduction target, fuel switching leads to an increase in upstream emissions that offsets 5% of the targeted reductions in 2050. When fuel extraction, processing, and transport emissions are included under the reduction target, accounting for 20% of overall NO_x reduction goal, the resulting control strategy meets the required reductions and does so at 35% lower cost by 2050. However, manufacturing and construction emissions increase and offset up to 7% of NO_x reductions in electric sector, indicating that it may be beneficial to consider these sources as well. Assuming no legal obstacles exist, life-cycle-based approaches could be implemented by allowing industry to earn reduction credits for reducing upstream emissions. We discuss some of the limitations of such an approach, including the difficulty in identifying the location of upstream emissions, which may occur across regulatory authorities or even outside of the U.S.

Air pollution control strategies directly limiting national health damages in the US

Exposure to fine particulate matter (PM2.5) from fuel combustion significantly contributes to global and US mortality. Traditional control strategies typically reduce emissions for specific air pollutants and sectors to maintain pollutant concentrations below standards. Here we directly set national PM2.5 mortality cost reduction targets within a global human-earth system model with US state-level energy systems, in scenarios to 2050, to identify endogenously the control actions, sectors, and locations that most cost-effectively reduce PM2.5 mortality. We show that substantial health benefits can be cost-effectively achieved by electrifying sources with high primary PM2.5 emission intensities, including industrial coal, building biomass, and industrial liquids. More stringent PM2.5 reduction targets expedite the phaseout of high emission intensity sources, leading to larger declines in major pollutant emissions, but very limited co-benefits in reducing CO2 emissions. Control strategies limiting health damages achieve the greatest emission reductions in the East North Central and Middle Atlantic states.

State-level drivers of future fine particulate matter mortality in the United States

Future fine particulate matter (PM₂.₅) concentrations and resulting health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographcs. In this study, a human-earth system model is used to estimate PM_2.5 mortality costs (PMMC) due to air pollutant emissions from each US state over the period 2015 to 2050, considering current major air quality and energy regulations. Contributions of various socioeconomic and energy factors to PMMC are quantified using the Logarithmic Mean Divisia Index. National PMMC are estimated to decrease 25% from 2015 to 2050, driven by decreases in energy intensity and PMMC per unit consumption of electric sector coal and transportation liquids. These factors together contribute 68% of the decrease, primarily from technology improvements and air quality regulations. States with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PMMC from their emissions. In contrast, states with larger projected decreases in PMMC have smaller increases in population and per capita GDP, and greater decreases in electric sector coal share and PMMC per unit fuel consumption.

Market Sensitivity of Solar-Fossil Hybrid Electricity Generation to Price, Efficiency, Policy, and Fuel Projections

Ideally, new electricity generating units will have low capital costs, low fuel costs, minimal environmental impacts, and satisfy demand without concerns of intermittency. When expanding generating capacity, candidate technologies can be evaluated against criteria such as these. Alternatively, it may be possible to pair technologies in such a way that the combination addresses these criteria better than either technology individually. One such approach is to pair concentrated solar power and natural gas combined-cycle units. This paper analyzes how an integrated solar combined cycle (ISCC) facility could fare in the larger US electricity production market, although the results are generalizable to a wider range of technologies. Modeling results suggest that a critical consideration is the extent to which ISCC qualifies as being renewable under state-level renewable portfolio standards (RPSs). The technology would be utilized at a higher level if it fully satisfies an RPS; however, even if the technology does not satisfy an RPS, it would be market-competitive if optimistic goals for capital cost and avoided natural gas purchases are met. Furthermore, if used in parts of the country with strong solar resources, ISCC could produce as much as 14% of national electricity generation in 2050. Whether adoption of ISCC leads to reduced air pollutant and greenhouse gas emissions is dependent on the technologies it displaces. Under default assumptions, the new ISCC capacity primarily displaces renewable and natural gas facilities as opposed to facilities with higher air pollutant emissions. Thus, the air pollution benefits of ISCC may be limited.

Evolution of the United States Energy System and Related Emissions under Varying Social and Technological Development Paradigms: Plausible Scenarios for Use in Robust Decision Making

The energy system is the primary source of air pollution. Thus, evolution of the energy system into the future will affect society's ability to maintain air quality. Anticipating this evolution is difficult because of inherent uncertainty in predicting future energy demand, fuel use, and technology adoption. We apply scenario planning to address this uncertainty, developing four very different visions of the future. Stakeholder engagement suggested that technological progress and social attitudes toward the environment are critical and uncertain factors for determining future emissions. Combining transformative and static assumptions about these factors yields a matrix of four scenarios that encompass a wide range of outcomes. We implement these scenarios in the U.S. Environmental Protection Agency MARKet ALlocation (MARKAL) model. Results suggest that both shifting attitudes and technology transformation may lead to emission reductions relative to the present, even without additional policies. Emission caps, such as the Cross-State Air-Pollution Rule, are most effective at protecting against future emission increases. An important outcome of this work is the scenario-implementation approach, which uses technology-specific discount rates to encourage scenario-specific technology and fuel choices. End-use energy demands are modified to approximate societal changes. This implementation allows the model to respond to perturbations in manners consistent with each scenario.

Estimating environmental co-benefits of U.S. low-carbon pathways using an integrated assessment model with state-level resolution

There are many technological pathways that can lead to reduced carbon dioxide emissions. However, these pathways can have substantially different impacts on other environmental endpoints, such as air quality and energy-related water demand. This study uses an integrated assessment model with state-level resolution of the energy system to compare environmental impacts of alternative low-carbon pathways for the United States. One set of pathways emphasizes nuclear energy and carbon capture and storage, while another set emphasizes renewable energy, including wind, solar, geothermal power, and bioenergy. These are compared with pathways in which all technologies are available. Air pollutant emissions, mortality costs attributable to particulate matter smaller than 2.5 μm in diameter, and energy-related water demands are evaluated for 50% and 80% carbon dioxide reduction targets in 2050. The renewable low-carbon pathways require less water withdrawal and consumption than the nuclear and carbon capture pathways. However, the renewable low-carbon pathways modeled in this study produce higher particulate matter-related mortality costs due to greater use of biomass in residential heating. Environmental co-benefits differ among states because of factors such as existing technology stock, resource availability, and environmental and energy policies.

Exploring the role of natural gas power plants with carbon capture and storage as a bridge to a low-carbon future

Natural gas combined-cycle (NGCC) turbines with carbon capture and storage (CCS) could be an important source of low-carbon electricity in the future. Factors affecting the market competitiveness of NGCC-CCS are examined by conducting a sensitivity analysis using the MARKet ALlocation energy system optimization model. The results indicate that widespread deployment of NGCC-CCS is better suited for a 30% energy system greenhouse gas (GHG) reduction trajectory than for a more stringent 50% reduction trajectory. Methane leakage rate, efficiency penalty, carbon dioxide (CO2) capture rate, and natural gas price are found to be the strongest factors influencing optimal NGCC-CCS deployment, in that order. NGCC plays an important role in meeting mid-term GHG targets across all model runs. A large portion of NGCC capacity is later retrofit with CCS, indicating that NGCC can be both a bridge to a low-carbon future and an integral part of that future. Thus, retrofitability and siting near CO2storage should be considerations as new NGCC capacity is built. Regional results indicate that NGCC-CCS deployment would be greatest in the West South Central region, followed by the East North Central region. In a business-as-usual scenario, both regions have considerable electricity production from fossil fuels. Conventional coal and gas capacity are displaced under a GHG reduction target, opening the door for NGCC-CCS in these regions. NGCC-CCS market penetration is projected to have a mixed impact on air pollutant emissions and energy-related water consumption. Whether impacts are positive or negative depends on the technologies displaced by NGCC-CCS.

Projecting state-level air pollutant emissions using an integrated assessment model: GCAM-USA

Integrated Assessment Models (IAMs) characterize the interactions among human and earth systems. IAMs typically have been applied to investigate future energy, land use, and emission pathways at global to continental scales. Recent directions in IAM development include enhanced technological detail, greater spatial and temporal resolution, and the inclusion of air pollutant emissions. These developments expand the potential applications of IAMs to include support for air quality management and for coordinated environmental, climate, and energy planning. Furthermore, these IAMs could help decision makers more fully understand tradeoffs and synergies among policy goals, identify important cross-sector interactions, and, via scenarios, consider uncertainties in factors such as population and economic growth, technology development, human behavior, and climate change. A version of the Global Change Assessment Model with U.S. state-level resolution (GCAM-USA) is presented that incorporates U.S.-specific emission factors, pollutant controls, and air quality and energy regulations. Resulting air pollutant emission outputs are compared to U.S. Environmental Protection Agency 2011 and projected inventories. A Quality Metric is used to quantify GCAM-USA performance for several pollutants at the sectoral and state levels. This information provides insights into the types of applications for which GCAM-USA is currently well suited and highlights where additional refinement may be warranted. While this analysis is specific to the U.S., the results indicate more generally the importance of enhanced spatial resolution and of considering national and sub-national regulatory constraints within IAMs.

Marginal abatement cost curve for nitrogen oxides incorporating controls, renewable electricity, energy efficiency, and fuel switching

A marginal abatement cost curve (MACC) traces out the relationship between the quantity of pollution abated and the marginal cost of abating each additional unit. In the context of air quality management, MACCs are typically developed by sorting control technologies by their relative cost-effectiveness. Other potentially important abatement measures such as renewable electricity, energy efficiency, and fuel switching (RE/EE/FS) are often not incorporated into MACCs, as it is difficult to quantify their costs and abatement potential. In this paper, a U.S. energy system model is used to develop a MACC for nitrogen oxides (NO_x) that incorporates both traditional controls and these additional measures. The MACC is decomposed by sector, and the relative cost-effectiveness of RE/EE/FS and traditional controls are compared. RE/EE/FS are shown to have the potential to increase emission reductions beyond what is possible when applying traditional controls alone. Furthermore, a portion of RE/EE/FS appear to be cost-competitive with traditional controls.

IMPLICATIONS

Renewable electricity, energy efficiency, and fuel switching can be cost-competitive with traditional air pollutant controls for abating air pollutant emissions. The application of renewable electricity, energy efficiency, and fuel switching is also shown to have the potential to increase emission reductions beyond what is possible when applying traditional controls alone.

Effects of recent energy system changes on CO2 projections for the United States

Recent projections of future United States carbon dioxide (CO₂) emissions are considerably lower than projections made just a decade ago. A myriad of factors have contributed to lower forecasts, including reductions in end-use energy service demands, improvements in energy efficiency, and technological innovations. Policies that have encouraged these changes include renewable portfolio standards, corporate vehicle efficiency standards, smart growth initiatives, revisions to building codes, and air and climate regulations. Understanding the effects of these and other factors can be advantageous as society evaluates opportunities for achieving additional CO₂ reductions. Energy system models provide a means to develop such insights. In this analysis, the MARKet ALlocation (MARKAL) model was applied to estimate the relative effects of various energy system changes that have happened since the year 2005 on CO₂ projections for the year 2025. The results indicate that transformations in the transportation and buildings sectors have played major roles in lowering projections. Particularly influential changes include improved vehicle efficiencies, reductions in projected travel demand, reductions in miscellaneous commercial electricity loads, and higher efficiency lighting. Electric sector changes have also contributed significantly to the lowered forecasts, driven by demand reductions, renewable portfolio standards, and air quality regulations.

Analysis of alternative pathways for reducing nitrogen oxide emissions

Strategies for reducing tropospheric ozone (O3) typically include modifying combustion processes to reduce the formation of nitrogen oxides (NOx) and applying control devices that remove NOx from the exhaust gases of power plants, industrial sources and vehicles. For portions of the U.S., these traditional controls may not be sufficient to achieve the National Ambient Air Quality Standard for ozone. We apply the MARKet ALlocation (MARKAL) energy system model in a sensitivity analysis to explore whether additional NOx reductions can be achieved through extensive electrification of passenger vehicles, adoption of energy efficiency and conservation measures within buildings, and deployment of wind and solar power in the electric sector. Nationally and for each region of the country, we estimate the NOx implications of these measures. Energy efficiency and renewable electricity are shown to reduce NOx beyond traditional controls. Wide-spread light duty vehicle electrification produces varied results, with NOx increasing in some regions and decreasing in others. However, combining vehicle electrification with renewable electricity reduces NOx in all regions.

IMPLICATIONS

State governments are charged with developing plans that demonstrate how air quality standards will be met and maintained. The results presented here provide an indication of the national and regional NOx reductions available beyond traditional controls via extensive adoption of energy efficiency, renewable electricity, and vehicle electrification.

Global climate change: the quantifiable sustainability challenge

Population growth and the pressures spawned by increasing demands for energy and resource-intensive goods, foods, and services are driving unsustainable growth in greenhouse gas (GHG) emissions. Recent GHG emission trends are consistent with worst-case scenarios of the previous decade. Dramatic and near-term emission reductions likely will be needed to ameliorate the potential deleterious impacts of climate change. To achieve such reductions, fundamental changes are required in the way that energy is generated and used. New technologies must be developed and deployed at a rapid rate. Advances in carbon capture and storage, renewable, nuclear and transportation technologies are particularly important; however, global research and development efforts related to these technologies currently appear to fall short relative to needs. Even with a proactive and international mitigation effort, humanity will need to adapt to climate change, but the adaptation needs and damages will be far greater if mitigation activities are not pursued in earnest. In this review, research is highlighted that indicates increasing global and regional temperatures and ties climate changes to increasing GHG emissions. GHG mitigation targets necessary for limiting future global temperature increases are discussed, including how factors such as population growth and the growing energy intensity of the developing world will make these reduction targets more challenging. Potential technological pathways for meeting emission reduction targets are examined, barriers are discussed, and global and US. modeling results are presented that suggest that the necessary pathways will require radically transformed electric and mobile sectors. While geoengineering options have been proposed to allow more time for serious emission reductions, these measures are at the conceptual stage with many unanswered cost, environmental, and political issues. Implications: This paper lays out the case that mitigating the potential for catastrophic climate change will be a monumental challenge, requiring the global community to transform its energy system in an aggressive, coordinated, and timely manner. If this challenge is to be met, new technologies will have to be developed and deployed at a rapid rate. Advances in carbon capture and storage, renewable, nuclear, and transportation technologies are particularly important. Even with an aggressive international mitigation effort, humanity will still need to adapt to significant climate change.

GLIMPSE: a rapid decision framework for energy and environmental policy

Over the coming decades, new energy production technologies and the policies that oversee them will affect human health, the vitality of our ecosystems, and the stability of the global climate. The GLIMPSE decision model framework provides insights about the implications of technology and policy decisions on these outcomes. Using GLIMPSE, decision makers can identify alternative techno-policy futures, examining their air quality, health, and short- and long-term climate impacts. Ultimately, GLIMPSE will support the identification of cost-effective strategies for simultaneously achieving performance goals for these metrics. Here, we demonstrate the utility of GLIMPSE by analyzing several future energy scenarios under existing air quality regulations and potential CO2 emission reduction policies. We find opportunities for substantial cobenefits in setting both climate change mitigation and health-benefit based air quality improvement targets. Though current policies which prioritize public health protection increase near-term warming, establishing policies that also reduce greenhouse gas emissions may offset warming in the near-term and lead to significant reductions in long-term warming.

Evaluating the effects of climate change on summertime ozone using a relative response factor approach for policymakers

The impact of climate change on surface-level ozone is examined through a multiscale modeling effort that linked global and regional climate models to drive air quality model simulations. Results are quantified in terms of the relative response factor (RRF(E)), which estimates the relative change in peak ozone concentration for a given change in pollutant emissions (the subscript E is added to RRF to remind the reader that the RRF is due to emission changes only). A matrix of model simulations was conducted to examine the individual and combined effects offuture anthropogenic emissions, biogenic emissions, and climate on the RRF(E). For each member in the matrix of simulations the warmest and coolest summers were modeled for the present-day (1995-2004) and future (2045-2054) decades. A climate adjustment factor (CAF(C) or CAF(CB) when biogenic emissions are allowed to change with the future climate) was defined as the ratio of the average daily maximum 8-hr ozone simulated under a future climate to that simulated under the present-day climate, and a climate-adjusted RRF(EC) was calculated (RRF(EC) = RRF(E) x CAF(C)). In general, RRF(EC) > RRF(E), which suggests additional emission controls will be required to achieve the same reduction in ozone that would have been achieved in the absence of climate change. Changes in biogenic emissions generally have a smaller impact on the RRF(E) than does future climate change itself The direction of the biogenic effect appears closely linked to organic-nitrate chemistry and whether ozone formation is limited by volatile organic compounds (VOC) or oxides of nitrogen (NO(x) = NO + NO2). Regions that are generally NO(x) limited show a decrease in ozone and RRF(EC), while VOC-limited regions show an increase in ozone and RRF(EC). Comparing results to a previous study using different climate assumptions and models showed large variability in the CAF(CB).

IMPLICATIONS

We present a methodology for adjusting the RRF to account for the influence of climate change on ozone. The findings of this work suggest that in some geographic regions, climate change has the potential to negate decreases in surface ozone concentrations that would otherwise be achieved through ozone mitigation strategies. In regions of high biogenic VOC emissions relative to anthropogenic NO(x) emissions, the impact of climate change is somewhat reduced, while the opposite is true in regions of high anthropogenic NO(x) emissions relative to biogenic VOC emissions. Further, different future climate realizations are shown to impact ozone in different ways.

Optimization of activated sludge designs using genetic algorithms

We describe a framework in which a genetic algorithm (GA) and a static activated sludge (AS) treatment plant design model (WRC AS model) are used to identify low cost activated sludge designs that meet specified effluent limits (e.g. for BOD, N, and P). Once the user has chosen a particular process (Bardenpho, Biodenipho, UCT or SBR), this approach allows the parameterizations for each AS unit process to be optimized systematically and simultaneously. The approach is demonstrated for a wastewater treatment plant design problem and the GA-based performance is compared to that of a classical nonlinear optimization approach. The use of GAs for multiobjective problems such as AS design is demonstrated and their application for reliability-based design and alternative generation is discussed.

Application of genetic algorithms for the design of ozone control strategies

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practically of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.